class_docstr: RNN encoder encoding each word via a bidirectional LSTM over character
  embeddings to obtain word representations, and then an LSTM (optionally
  bidirectional in first layer) to combine word representations.
  
  Uses nn.LSTM for cuDNN support / ONNX exportability.
docstr: Reorder all outputs according to new_order.
docstr_tok: ["Reorder","all","outputs","according","to","new_order","."]
code: def reorder_encoder_out(self, encoder_out, new_order):
      """Reorder all outputs according to new_order."""
      return rnn.reorder_encoder_output(encoder_out, new_order)
  
imports: import logging
  from ast import literal_eval
  import torch
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture
  from pytorch_translate import char_encoder, model_constants, rnn, utils, vocab_constants
  from pytorch_translate.common_layers import Embedding, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 3 for calling the CharRNNEncoder obj's underlying member method code with example usage: obj.reorder_encoder_out(encoder_out, new_order) and returns: rnn